论文标题
自适应频率先验的频率选择性重建图像从非规范子采样
Adaptive frequency prior for frequency selective reconstruction of images from non-regular subsampling
论文作者
论文摘要
图像信号通常定义在矩形二维网格上。但是，存在没有实现此方案的情况，而图像信息仅可用于像素位置的非规范子集。为了处理，传输或显示此类图像信号，需要重新采样到常规网格。最近，已提出频率选择性重建（FSR）是一种非常有效的基于稀疏性的算法，用于解决此不确定的问题。为此，FSR迭代在傅立叶域中生成信号的模型。在这种情况下，使用光传递函数启发的固定频率用于偏爱低频内容。但是，这种固定的先验通常太严格了，可能导致重建质量降低。为了解决这一弱点，本文提出了自适应频率先验，将可用样本的局部密度考虑在内。提出的自适应先验允许具有很高的重建质量，与固定先验相比，相比，与可用样品的密度无关。与其他最先进的算法相比，几个DB的视觉上明显增益是可能的。
Image signals typically are defined on a rectangular two-dimensional grid. However, there exist scenarios where this is not fulfilled and where the image information only is available for a non-regular subset of pixel position. For processing, transmitting or displaying such an image signal, a re-sampling to a regular grid is required. Recently, Frequency Selective Reconstruction (FSR) has been proposed as a very effective sparsity-based algorithm for solving this under-determined problem. For this, FSR iteratively generates a model of the signal in the Fourier-domain. In this context, a fixed frequency prior inspired by the optical transfer function is used for favoring low-frequency content. However, this fixed prior is often too strict and may lead to a reduced reconstruction quality. To resolve this weakness, this paper proposes an adaptive frequency prior which takes the local density of the available samples into account. The proposed adaptive prior allows for a very high reconstruction quality, yielding gains of up to 0.6 dB PSNR over the fixed prior, independently of the density of the available samples. Compared to other state-of-the-art algorithms, visually noticeable gains of several dB are possible.
